Image registration is the process of combining one or more sets of data to form a single data representation. Data sets may be multiple photographs, data from different sensors, data from different times, or data stored in different formats. Image registration techniques may involve combining two or more images, or selected points from the images, to produce a composite image containing data from each of the original images. Some image registration techniques may project details from one data set (referred to as the target) onto a second data set (referred to as the reference). Some image registration techniques may compare or transform a target image to align with one or more stored reference images. These image registration techniques may use algorithms to relate points between images so that related points or structures in the images are correlated in the resulting composite image.
Some methods of image registration search for detailed correspondences between particular features in the images such as points, lines and contours that appear similar. These appearance-based methods use discovered correspondences to transform or map a target image to one or more reference images. Such techniques may involve input from individual with expertise in the type of object, scene or structure represented in the images. The expert may identify a set of landmark features in the images that the registration technique should attempt to correlate. For example, an image registration method may compare two MRI images of different axial slices of a human head, and a physician may identify points (and/or contours surrounding these points) that corresponding to the cerebellum (a landmark) in the two images. The image registration algorithm may then map the target image to the reference image by relying on a known relationship between landmarks. Thus, by matching visible landmarks in the target image with previously identified landmarks in the reference image, the registration technique can draw conclusions about how the target image aligns with the reference image.
Other methods of image registration compare detailed volume-based (or 3D geometric) imagery in images using correlation metrics. Some of these geometry-based methods may then measure a distance that represents the disparity between a target image and a reference image based on how closely the volume-based features align. Registration of the two images may utilize an optimization equation that aids in finding a mapping that reduces this distance measurement. These methods may register entire images or sub-images, and if sub-images are registered, the sub-images may be treated as corresponding feature points.
Some other methods of image registration have used geospatial information to provide a reference source of structure or features. Geospacial information (or geolocation) refers generally to the identification of the real-world geographic location of an object. Geolocation may refer to the practice of assessing the location, or to the actual assessed location. Geospatial information may indicate ties between features in photographs and the actual geographical location such features or structures. For example, some current place recognition algorithms use GPS-tagged, crowd-sourced image collections from online repositories coupled with direct feature matching techniques and multi-view geometry. A target image may be taken of a street corner or a monument and then the place recognition algorithm attempts to find the most similar feature in a reference database by scanning a large number of saved reference images. These place recognition algorithms require large-scale image databases to enable such geolocation.